Apparatus and methods for manipulation and optimization of biological systems

ABSTRACT

A system includes a processor and a memory storing processor-executable instructions, which when executed by the processor direct the processor to provide control data indicating application of a stimulus to a biological system, obtain sensor data indicating measurements of a response of the biological system to the stimulus, determine fitting parameters of a biological system model based on the response of the biological system to the stimulus, and predict a magnitude of the stimulus that, when applied to the biological system, will yield an optimized response of the biological system based on the model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.14/281,664, filed May 19, 2014, which is a continuation of U.S.Application No. 13/562,216, filed Jul. 30, 2012, now issued as U.S. Pat.No. 8,765,459, which is a divisional application of U.S. applicationSer. No. 11/719,749, filed Aug. 4, 2008, now issued as U.S. Pat. No.8,232,095, which is the national phase of PCT Application No.PCT/US2005/042096, filed on Nov. 18, 2005 which claims the benefit ofpriority of U.S. provisional application Ser. No. 60/629,500 filed Nov.18, 2004, and all of which are expressly incorporated herein byreference in their entirety and for all purposes.

STATEMENT OF FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under NCC2-1364 awardedby the National Aeronautics and Space Administration. The Government hascertain rights in the invention.

TECHNICAL FIELD

The invention provides apparatus and methods for conducting automaticanalysis and manipulation, e.g., experiments and systems optimization,on biological samples, e.g., viable biological samples, including, butnot limited to cells, tissues, organs cultures and the like, e.g., plantand mammalian cells, a cell culture, cell fragments and/or cellorganelles, a tissue, an isolated organ, a microorganism, e.g.,bacteria, protozoa, yeast and viruses.

In alternative aspects, systems include a device for sustaining cells ortissue samples, one or more actuators for stimulating the samples viabiochemical, electromagnetic, thermal, mechanical, and/or opticalstimulation, one or more sensors for measuring a biological responsesignal of the samples resulting from the stimulation of the sample. Inone aspect, the systems and methods of the invention use at least oneoptimization algorithm to modify the actuator's control inputs forstimulation, responsive to the sensor's output of response signals. Thecompositions and methods of the invention can be used, e.g., for, butnot limited to, the following applications: systems optimization of anybiological manufacturing or experimental system, e.g., bioreactors forproteins, polypeptides or peptides for vaccines, and the like, directingdifferentiation of cells to a specific property, e.g., therapeuticproteins, polypeptides or peptides for vaccines, and the like, anddetermining specific combination of ligands and their correspondingconcentrations in drug screening systems, small molecules (e.g.,antibiotics), polysaccharides, lipids, and the like.

BACKGROUND

Sophisticated biological systems, as often occur in nature, such ascells, tissues, and organs in the human body for example, are capable ofresponding to external chemical and/or physical stimuli (“inputvariables” in some systems parlance). Such stimuli can additionally beapplied as spatial or temporal gradients. The responses of biologicalsystems can nonlinearly depend on the stimuli in a complex interplay ofmultiple variables comprising external stimuli from the environment andinternal factors within the biological system. This interplay caninvolve synergistic and antagonistic relationship among the multipleinput variables.

As a result of such complexity, it can be difficult to manipulate abiological system, such as a cell, a group of cells, and organ ortissue, to behave in a desirable or nearly optimal way withoutunderstanding the following: (i) the effects that each input variable(e.g., type of control input) has on a system, (ii) the differentpossible states of each variable (specific parameter of the controlinput), (iii) how those states affect the overall system, and (i v) theeffects of interactions among the variables. The inability to manipulatea complex biological system posts challenges to elucidating mechanismsof cellular processes although such information can contributesignificantly to the advancement of basic research and medicalapplications. Further, cellular processes can be dynamic, stochastic,nonlinear, multi-parametric, and/or possess memory effects. An exampleis cells which regulate their activities by integrating multipleexternal stimuli using internal and external cellular signaltransduction networks. A signal transduction network is a cascade ofbiochemical reactions in the cells that can modify the cellularactivity, such as a transcription factor activity in response to thebinding of an external stimulus such as a ligand that binds to acorresponding receptor.

In a biological experiment for understanding the above-described signaltransduction pathway, each stimulus, for example a ligand such as a drugor a cytokine, or other changes in the physical or chemical environment,can be tested independently, via an independent variation approach.Elements in signal transduction pathways that responds to a stimulus canbe identified in an effort to reconstruct the signal transductionpathway and associated biological responses from downstream cellularprocesses. Due to the complexity of biological systems, the transductionpathway identification and reconstruction process can be extremelytime-consuming and typically provides only partial information on thepathway. Furthermore, interactions between or among stimuli can bemissed by such an independent variation approach. Therefore, importantinformation in the combinatorial control, which can occur naturally inbiological systems, is unavailable.

In combinatorial control, the combinations of the inputs can interactnonlinearly, in an antagonistic and/or a synergistic fashion. Instead ofone to one correspondence of the input and output relationships,specific combinations can result in different responses. For example, itis estimated that only about 160 transcription factors exist in theyeast genome, but yeast cells contain thousands of co-regulated sets ofgenes: Further, it is difficult to determine combinations of controlinputs, such as a specific ligands and specific concentrations of theligands, for eliciting a desired response from a biological samplerepresenting a biological system. Examples of other biologicalexperiments that illustrate problems associated with the determinationof combinatorial control include specific combinations of drugs andtheir corresponding concentrations in combination drug treatment, or inidentifying the proper combination of environment cues in nature for aspecific biological response.

One approach is to test all the combinations of the different stimuli(e.g.; different ligands, or chemical or physical conditions) and thedifferent states of each stimulus (e.g., different concentrations of aspecific ligand, or different values of pH, temperature, shear stress,electrical field, magnetic field, etc.). However, the number of tests(experiments) required increases exponentially with the number ofdifferent stimuli (or “input variables” or “control inputs” in somesystems terminology). The number of tests can become impracticably largein terms of cost and time for large numbers of input variables. Forexample, testing the effectiveness of a six-drug combination cocktail ona tissue or cell sample, assuming only ten different concentrations perdrug is used, requires 10⁶ or one million tests in order to identify anearly optimal blend of concentrations. The identification ofsubstantially optimal stimulus conditions for a desired biologicalresponse with a more limited number of tests is very desirable.

For decades, flu vaccines have been manufactured growing viruses inmillions of live, fertilized eggs. The system works well, but it is timeconsuming and hard to ramp up quickly in a public health emergency. Thethreat of a flu pandemic demands new methods for developing newer,faster production systems for vaccines, including vaccines for flu, SARSand the like. Cell-based production methods grow the flu virus in steelvats filled with living cells derived from monkeys, dogs, humans or eveninsects. Some vaccines produced this way have won limited approval inEurope, but none has been cleared for use in the United States.

SUMMARY

The invention provides systems and methods manipulating, controlling,optimizing biological systems, e.g., for eliciting a more desiredbiological response of biological sample, such as a tissue, organ,and/or a cell. In one aspect, systems and methods of the inventionoperate by monitoring stimuli and conditions in a biosystem, includingparameters or representative biological samples sustained in the system.In alternative aspects, systems of the invention include a device forsustaining cells or tissue samples, one or more actuators forstimulating the samples via biochemical, electromagnetic, thermal,mechanical, and/or optical stimulation, one or more sensors formeasuring a biological response signal from or condition in a sampleresulting from the growth and/or stimulation of the sample. In oneaspect, the systems and methods of the invention comprise at least onealgorithm, e.g., an optimization algorithm such as a smart non-linearoptimization search algorithm, e.g., in the context of an interactivesoftware and hardware system, e.g., as a computer implemented method.

The algorithm, e.g., optimization algorithm, can be used to monitorand/or control any or all aspects of a system, e.g., a biosystem,including environmental parameters, system inputs and system outputs. Inone aspect, a smart non-linear optimization search algorithm is used tomonitor stimuli and conditions in a biosystem, e.g., to modify theactuator's control inputs for stimulation responsive to a sensor'soutput of response signals. Smart non-linear optimization searchalgorithms used in the systems and methods of the invention cancomprise, e.g., a simulated annealing algorithm, a stochastic localsearch algorithm, a stochastic hill-climbing algorithm, aMetropolis-Hastings sampler algorithm, a greedy randomized adaptivesearch algorithm, an evolutionary algorithm, a genetic algorithm, ataboo search algorithm, and/or a Gur Game algorithm, or any combinationthereof.

The compositions and methods of the invention can be used, e.g., for,but not limited to, the following applications: systems manipulationand/or optimization of any biological manufacturing or experimentalsystem, e.g., bioreactors for proteins, polypeptides or peptides forvaccines, and the like, directing differentiation of cells to a specificproperty, and determining specific combination of ligands and theircorresponding concentrations in drug screening systems. The compositionsand methods of the invention can be used, e.g., to for systemsoptimization of any biological manufacturing or experimental system,e.g., bioreactors for proteins, e.g., therapeutic proteins, polypeptidesor peptides for vaccines, and the like, small molecules (e.g.,antibiotics), polysaccharides, lipids, and the like.

Some embodiments of the current invention provide systems and methods toautomatically, interactively, and/or experimentally determine optimalcontrol in puts (values of variables) for eliciting a desired biologicalsystem response, e.g., cell differentiation, drug cocktail, anddetermining specific combination of ligands and their correspondingconcentrations in drug screening system. These embodiments can provide away to determine the variables, the (e.g., types of control in put) thatare important for a desired system response, and states of eachimportant variable (specific parameter of the control input) that elicita substantially optimal biological system response. These embodimentscan obviate the need to run a large number of tests, therebydramatically reducing the time and resources required to identify inputvariables and corresponding values to elicit an optimized, e.g., asubstantially optimum (or nearly optimized—i.e., minimal optimization issufficient) biological system response.

In one embodiment, a system of the invention comprises: (i) a cell,organ or tissue culture device, a bioreactor, an artificial organsystem, including similar devices or systems, for sustaining biologicalsamples representative of (e.g., derived from) the biological system tobe studied or manipulated, as in a manufacturing biosystem; (ii) one ormore actuators for stimulating the samples, e.g., via chemical,electromagnetic, thermal, mechanical, optical, and/or otherenvironmental stimulation; (iii) one or more sensors for measuring theresponse signal of the samples resulting from the stimulation; and (iv)a controller executing a smart search algorithm to modify the actuator'scontrol inputs for subsequent biological sample manipulation and/orstimulation responsive to the sensors' outputs responsive to biologicalsample responses.

Embodiments can also comprise appropriate software and hardwarecomponents to provide user-interface and data analysis capabilities toenable user monitor and control of the automatic optimization process.Thus, the invention comprises computer program products for implementingsystem optimization comprising smart non-linear optimization searchalgorithms, e.g., a simulated annealing algorithm, a stochastic localsearch algorithm, a stochastic hill-climbing algorithm, aMetropolis-Hastings sampler algorithm, a greedy randomized adaptivesearch algorithm, an evolutionary algorithm, a genetic algorithm, ataboo search algorithm, and/or a Gur Game algorithm, or any combinationthereof. The invention also provides computer implemented methodscomprising these algorithms for automatic optimization processes,including for the monitoring and/or control of experimental andmanufacturing biosystems.

Embodiments can utilize biological system response information obtainedfrom the measurements by the sensors. Based partly on such information,in some embodiments a stochastic search algorithm (e.g., a smartnon-linear optimization search algorithm, such as a Gur Game searchalgorithm) can iteratively determine the input variables for achieving adesired biological system, response. Cells or tissue samples can besustained in a device that can maintain desired physical and chemicalstates of the biological samples for a predetermined (e.g., extended)period of time. Actuators, including biochemical and physical, can beprovided to generate temporal and/or spatial stimulation. The states andresponses of the samples can be monitored with instruments and/orsensors. In one aspect, a controller implementing an algorithm, e.g., anoptimization algorithm such as a stochastic search algorithm, is used toautomatically and iteratively modify the initial inputs in search of amore nearly optimal biological system response, e.g., output from abioreactor, e.g., a microfluidic cell culture.

Four exemplary applications of embodiments of this invention comprisecompositions and methods for the analysis, manipulation and/oroptimization of: (i) systems for the manufacturing of polypeptides(e.g., antibodies or cytokines), small proteins (e.g., antigens forvaccines), small molecules (e.g., antibiotics, co-factors or vitamins),lipids, nucleic acids, sugars and polysaccharides, and other molecules;(ii) systems for directed cell differentiation and/or proliferation,such as in tissue engineering; (iii) systems for combination drugdiscovery or drug validation, e.g., using cocktails of candidate drugs;and (iv) systems for discovery in cell biology.

The invention provides systems for automatic manipulation of abiological sample (any biological material, such as a tissue, a cell, avirus, a plasmid and the like), comprising:

(a) a product of manufacture operable to sustain a biological samplepositioned therein; (b) at least one stimulator operable to alter atleast one parameter to which the biological sample is exposed; (c) atleast one sensor operable to measure the at least one parameter; and (d)a controller operably connected to receive a signal from the at leastone sensor such that the controller is responsive to the signal receivedfrom the sensor to actuate the at least one stimulator to which it isoperably connected, and the stimulator upon actuation provides astimulus to alter the at least one parameter measured in step (c), or atleast one other parameter, or a combination thereof, within or affectingthe product of manufacture, thereby manipulating the biological sample,wherein the controller comprises or is operably connected to analgorithm to automatically determine the stimulus given to thebiological sample, and the determination is based at least in part onthe at least one parameter measured by the sensor in step (c).

In one aspect, the algorithm is an optimization algorithm or amanipulation algorithm. The controller can comprise or is operablyconnected to a computer program product comprising the algorithm toautomatically determine the stimulus given to the biological sample. Thecomputer program product can be operably contained within (e.g.,programmed into) a microchip, a microprocessor, a computer or acombination thereof. In one aspect, the controller comprises or isoperably connected to an analog neural network, a hot wire circuitry, amicroprocessor, a computer or a combination thereof, comprising thealgorithm to automatically determine the stimulus given to thebiological sample. In one aspect, the optimization algorithm comprisescomputer program product as set forth in FIG. 6; or, the optimizationalgorithm has a logic flow as set forth in FIG. 6.

In one aspect, the biological sample comprises a cell, a cell culture, atissue, an isolated organ or an organ system or a cell isolate. The cellcan be a bacterial cell, a protozoan cell, an insect cell, a yeast cell,a plant cell or a mammalian cell, wherein optionally the mammalian cellis a human cell. In one aspect, the product of manufacture comprises abioreactor, e.g., a composition comprising a cell culture apparatus.

In one aspect, at least one parameter altered by the at least onestimulator comprises an environmental condition in the product ofmanufacture. The environmental condition can be altered by the at leastone stimulator in the product of manufacture comprises temperature, pH,oxygen or carbon dioxide concentration, nutrient or waste concentration,rate of exchange of cell or tissue culture nutrients, rate of harvestingor removal of a cell or tissue culture secreted product, cell growth ordifferentiation rate, cell concentration, or a combination thereof.

In one aspect, the stimulus applied in step (d) for altering the atleast one parameter measured in step (c), or at least one otherparameter, or a combination thereof, within the product of manufacture,or affecting the product of manufacture, comprises a biochemical,electromagnetic, thermal, mechanical and/or optical stimulation. In oneaspect, the stimulus applied in step (d) for altering the at least oneparameter measured in step (c), or at least one other parameter, or acombination thereof, within the product of manufacture comprises achange in temperature, pH, oxygen or carbon dioxide concentration, rateof exchange of cell or tissue culture nutrients, rate of harvesting orremoval of a cell or tissue culture secreted product, or a combinationthereof.

In one aspect, the algorithm is operably linked to the controller, thesensor and/or the stimulator, or any other apparatus or system to whichthe system of the invention is also linked with or communicating with.In one aspect, more than one algorithm is used, e.g., in parallel ormultiplex linked systems, e.g., for communicating with another apparatusor system to which the system of the invention is also linked with orcommunicating with.

In one aspect, the algorithm manipulates one or more parameters in thesystem to achieve a desired result from the biological sample or thesystem. The algorithm can comprise an optimization algorithm tomanipulate one or more parameters in the system to achieve an optimizeddesired result from the biological sample or the system. In alternativeaspects, the term “optimized” includes “nearly optimized”—i.e., onlyminimal optimization. In other words, an optimization by a system ormethod of the invention can include partial, or minimal, optimization,in addition to being capable, in some aspects, complete optimization.

In one aspect, the desired result comprises growing, sustaining and/ordifferentiating the biological sample in the product of manufacture. Thealgorithm can manipulate or optimize a quantitative aspect (the amountof) and/or a qualitative aspect (the nature of) of the stimulus used tomodify the one or more parameters in the system to achieve the desiredresult from the biological sample. In one aspect, the desired resultfrom the biological sample generated by the algorithm's manipulation ofthe system comprises manipulation or optimization of: cell growth, celldifferentiation, cell vitality, synthesis or secretion of a naturallyoccurring or recombinant protein, a small molecule, an antibiotic, apolysaccharide, a virus, a nucleic acid and/or a lipid. The naturallyoccurring or recombinant protein whose synthesis or secretion ismanipulated or optimized can comprise a cytokine, an n anti body, anantigen or a structural protein.

In one aspect, the algorithm is set to direct an iterative repetition ofmeasuring at least one parameter in the system, actuating at least onestimulator and measuring at least one parameter modified in response tothe stimulation, thereby manipulating and/or optimizing the biologicalsystem to achieve the desired result. The algorithm can be set to directa dynamic change in the time period of each iteration. In one aspect,the algorithm is set to self-organize and/or self-optimize the automaticmanipulation of the biological sample.

In one aspect, the algorithm comprises a smart non-linear optimizationsearch algorithm, such as a simulated annealing algorithm, a stochasticlocal search algorithm, a stochastic hill-climbing algorithm, aMetropolis-Hastings sampler algorithm, a greedy randomized adaptivesearch algorithm, an evolutionary algorithm, a genetic algorithm, ataboo search algorithm, and/or a Gur Game algorithm, or any combinationthereof, or variations thereof. The smart non-linear optimization searchalgorithm can be set as a biased random walk toward the desired result,and the desired result is set as a global optimum of a plurality ofparameters. The algorithm can set a global figure of merit (a rewardfunction) to measure the performance of the system as a whole to reachthe desired result. In one aspect, the algorithm can assign eachmeasured parameter any one of a pre-defined set of discrete states (oran automaton), and different inputs for each parameter, or differentinputs for the same parameter over time, result in a different number ofstates and state values being assigned to each parameter, and duringmanipulation or optimization or the biological system each automatonmoves from one state to another based on being rewarded for causing adesired response, and the reward probabilistically drives the system tothe desired result.

The invention provides methods for automatic manipulation oroptimization of a biological sample comprising (a) providing at leastone system of the invention; (b) measuring at least one parameter in thebiological sample, wherein the controller receives a signal from the atleast one sensor; and (c) providing a stimulus to the biological sampleto alter the at least one parameter measured in step (b), or at leastone other parameter, or a combination thereof, within the product ofmanufacture by running the algorithm, wherein the algorithm determinesthe stimulus given to the biological sample, and the determination isbased at least in part on the at least one parameter measured by thesensor in step (b), thereby automatically manipulating or optimizing thebiological sample. The invention also provides methods for automaticmanipulation or optimization of a biological sample to achieve a desiredresult comprising (a) providing at least one system of the invention;(b) providing a first stimulus to the biological sample; (c) measuringat least one parameter in the biological sample in response to the firststimulus, wherein the parameter is measured by the controller receivinga signal from the at least one sensor; and (d) providing at least asecond stimulus to the biological sample to alter the at least oneparameter measured in step (b), or at least one other para meter, or acombination thereof, to achieve a desired result, wherein the nature andamount of the at least second stimulus is determined and provided byrunning the algorithm, and the determination is based at least in parton the at least one para meter measured by the sensor in step (c). Inone aspect of any method of the invention, the steps of the method canbe iteratively repeated until a desired result is achieved.

In one aspect, the system used in any method of the invention comprisessustaining or growing a biological sample within an apparatus forgrowing or sustaining or handling a biological material, e.g., in anapparatus such as a bioreactor. In one aspect, the biological samplecomprises a cell, a cell culture, a tissue, an isolated organ or anorgan system or a cell isolate.

In one aspect of any method of the invention, the cell is a bacterialcell, a protozoan cell, an insect cell, a yeast cell, a plant cell or amammalian cell, wherein optionally the mammalian cell is a human cell.In one aspect of any method of the invention, the product of manufacturecomprises a bioreactor. The at least one parameter can be altered by theat least one stimulator, and the parameter can comprise an environmentalcondition in the product of manufacture or affecting the product ofmanufacture. In one aspect, the environmental condition altered by theat least one stimulator in the product of manufacture comprisestemperature, pH, oxygen or carbon dioxide concentration, nutrient orwaste concentration, rate of exchange of cell or tissue culturenutrients, rate of harvesting or removal of a cell or tissue culturesecreted product, cell growth or differentiation rate, cellconcentration, or a combination thereof The stimulus applied in a stepfor altering the at least one parameter measured, or at least one otherparameter, or a combination thereof, within the product of manufacturecan comprise a biochemical, electromagnetic, thermal, mechanical and/oroptical stimulation.

In one aspect of any method of the invention, the stimulus applied astep for altering the at least one parameter measured, or at least oneother parameter, or a combination thereof, within the product ofmanufacture comprises a change in temperature, pH, oxygen or carbondioxide concentration, rate of exchange of cell or tissue culturenutrients, rate of harvesting or removal of a cell or tissue culturesecreted product, or a combination thereof.

In one aspect of any method of the invention, the algorithm manipulatesone or more parameters in the system to achieve a desired result fromthe biological sample. The desired result can comprise growing,sustaining and/or differentiating the biological sample (e.g., cells,tissues) in the product of manufacture (e.g., a bioreactor).

In one aspect, the algorithm manipulates or optimizes a quantitativeaspect (the amount of) and/or a qualitative aspect (the nature of) ofthe stimulus used to modify the one or more parameters in the system toachieve the desired result from the biological sample. The desiredresult from the biological sample generated by the algorithm'smanipulation or optimization of the system can comprise manipulation oroptimization of: cell growth, cell differentiation, cell vitality,synthesis or secretion of a naturally occurring or recombinant protein,a small molecule, an antibiotic, a polysaccharide, a virus, a nucleicacid and/or a lipid. In one aspect, the naturally occurring orrecombinant protein whose synthesis or secretion is manipulated oroptimized comprises a cytokine, an antibody or a structural protein.

In one aspect, the algorithm is set to direct an iterative repetition ofmeasuring at least one parameter in the system, actuating at least onestimulator and measuring at least one parameter modified in response tothe stimulation, thereby manipulating and/or optimizing the biologicalsystem to achieve the desired result. The algorithm can be set to directa dynamic change in the time period of each iteration. In one aspect,the algorithm is set to self-organize and self-optimize the automaticmanipulation of the biological sample. The algorithm can comprise asmart non-linear optimization search algorithm, such as a simulatedannealing algorithm, a stochastic local search algorithm, a stochastichill-climbing algorithm, a Metropolis-Hastings sampler algorithm, agreedy randomized adaptive search algorithm, an evolutionary algorithm,a genetic algorithm, a taboo search algorithm, and/or a Gur Gamealgorithm, or any combination thereof

In one aspect, the smart non-linear optimization search algorithm is setas a biased random walk toward the desired result, and the desiredresult is set as a global optimum of a plurality of parameters. In oneaspect, the algorithm sets a global figure of merit (a reward function)to measures the performance of the system as a whole to reach thedesired result. The algorithm can assign each measured parameter any oneof a pre-defined set of discrete states (or an automaton), and differentin puts for each parameter, or different inputs for the same parameterover time, result in a different number of states and state values beingassigned to each parameter, and during manipulation and/or optimizationof the biological system each automaton moves from one state to anotherbased on being rewarded for causing a desired response, and the rewardprobabilistically drives the system to the desired result.

The invention provides systems for automatically manipulating oroptimizing production of a product by a biological system, comprising:(a) a bioreactor and a biological sample positioned therein; (b) atleast one stimulator operable to alter at least one parameter to whichthe biological sample is exposed; (c) at least one sensor operable tomeasure the at least one parameter; and (d) a controller operablyconnected to receive a signal from the at least one sensor such that thecontroller is responsive to the signal received from the sensor toactuate the at least one stimulator, and the stimulator upon actuationprovides a stimulus to alter the at least one parameter measured in step(c), or at least one other parameter, or a combination thereof, withinthe bioreactor, thereby manipulating the biological sample, wherein thecontroller comprises or is operably connected to a computer programproduct comprising an algorithm operably linked to the stimulator toautomatically determine the stimulus given to the biological sample, andthe determination is based at least in part on the at least oneparameter measured by the sensor in step (c) and the rate or amount ofproduct produced by a biological system, thereby automaticallymanipulating or optimizing production of the product by the biologicalsystem.

The invention provides computer-implemented methods for automaticallyoptimizing production of a product by a biological system comprising amethod of the invention. The invention provides computer programproducts for automatically optimizing production of a product by abiological system, wherein the computer program product comprises amachine-readable medium including machine-executable instructions, theinstructions being operative to cause a machine to run acomputer-implemented method of the invention. The invention providescomputer systems comprising a computer program product of the invention.

Other features and aspects of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresin accordance with embodiments of the invention. The summary is notintended to limit the scope of the invention, which is defined solely bythe claims attached hereto. The details of one or more embodiments ofthe invention are set forth in the accompanying drawings and thedescription below.

All publications, patents, patent applications, GenBank sequences andATCC deposits, cited herein are hereby expressly incorporated byreference for all purposes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high level block diagram of an exemplary embodiment of theinvention.

FIG. 2 illustrates an exemplary microfluidic bioreactor according to anembodiment of the invention.

FIG. 3 illustrates an exemplary embodiment of the invention at anintermediate level of detail.

FIG. 4 illustrates another embodiment of the invention, showingfluorescent and phase contrast microscope sensors.

FIG. 5 illustrates an environmental enclosure for some embodiments ofthe invention.

FIG. 6 is a flow chart illustrating an exemplary Gur Game searchalgorithm, according to an embodiment of the invention.

FIG. 7 shows a state diagram illustrating an exemplary Gur Game searchalgorithm that is used in an embodiment of the invention.

FIGS. 8A, 8B, and 8C illustrate a substantially optimal biologicalsystem response as generated by an exemplary system of the invention.

FIGS. 9A, 9B, 9C, 9D, 9E, and 9F illustrate a sensitivity analysis ofindividual cytokines in combination, as determined by an embodiment ofthe invention.

DETAILED DESCRIPTION

The invention provides systems and methods for manipulating, e.g.,optimizing and controlling, biological systems by using an optimizationalgorithm set for the automatic manipulation of the biological sample toelicit a desired response from the biological sample. The systems andmethods of the invention can be used to manipulate, control, or optimizeany biological system, including but not limited to, the followingapplications: systems optimization of a biological manufacturing orexperimental system, e.g., bioreactors for proteins, polypeptides orpeptides for vaccines, and the like, directing differentiation of cellsto a specific property, and determining specific combination of ligandsand their corresponding concentrations in drug screening systems. Theinvention provides apparatus and methods for conducting automaticanalysis and manipulation, e.g., experiments and systems optimization,on biological samples, e.g., viable biological samples, including, butnot limited to cells, tissues, organs cultures and the like, e.g., plantand mammalian cells, a cell culture, cell fragments and/or cellorganelles, a tissue, an isolated organ, a microorganism, e.g.,bacteria, protozoa, yeast and viruses. The invention provides apparatusand methods for conducting automatic analysis and manipulation and/orsystems optimization on manufacturing systems for biological products,such as proteins (e.g., cytokines, vaccines), small molecules (e.g.,antibiotics), lipids, polysaccharides, nucleic acids and the like. Theinvention provides apparatus and methods for conducting automaticanalysis and manipulation and/or systems optimization in tissueengineering systems, such as systems for directed cell proliferation ordifferentiation. The invention provides apparatus and methods forconducting automatic analysis and manipulation and/or systemsoptimization in drug discovery systems, including cell-based drugdiscovery or drug efficacy verification systems, particularly incombination drug discovery or efficacy validation, e.g., using cocktailsof candidate drug compounds. The invention provides apparatus andmethods for conducting automatic analysis and manipulation and/orsystems optimization in cell biology systems, including experimentalsystems, such as gene circuit, cell proliferation, programmed cell death(apoptosis) or signal transduction systems.

In one aspect, the systems and methods of the invention are used tooptimize the production of biological products, including polypeptideand peptides, e.g., antibodies, antigens or cytokines (includingrecombinant proteins), small molecules made by cell-based or in vitrobiological systems, lipids, polysaccharides, nucleic acids, viruses,recombinant vectors and the like. Thus, in one aspect, the systems andmethods of the invention are used to optimize the production of vaccinesand medicines. In another aspect, the systems and methods of theinvention are used to direct differentiation of cells into a group ofcells with specific properties such as having a specific surface marker.In another aspect, the systems and methods of the invention are used toidentify the specific combination of ligands and their correspondingconcentrations.

In the following description, reference is made to the accompanyingdrawings, which illustrate several embodiments of the present invention.It is understood that other embodiments may be utilized and mechanical,compositional, structural, electrical, and operational changes may bemade without departing from the spirit and scope of the presentdisclosure. The following detailed description is not to be taken in alimiting sense, and the scope of the embodiments of the presentinvention is defined only by the claims of the issued patent.

Some portions of the detailed description which follows are presented interms of procedures, steps, logic blocks, processing, and other symbolicrepresentations of operations on data bits that can be performed oncomputer memory. In alternative aspects, the systems of the inventioncomprise a computer implemented method, a procedure, a computer executedstep, a logic block, a process, etc., to be a self-consistent sequenceof steps or instructions leading to a desired result, as describedherein. In one aspect, the steps are those utilizing physicalmanipulations of physical quantities, e.g., parameters in a biologicalsystem. These quantities can take the form of electrical, magnetic, orradio signals capable of being stored, transferred, combined, compared,and otherwise manipulated by a computer system. In various aspects,these signals are referred to at times as bits, values, elements,symbols, characters, terms, numbers, or the like. Each step may beperformed by hardware, software, firm ware, human (operator)manipulation or any combinations thereof.

Embodiments of the present invention provide apparatus and methods todetermine the variables or parameters (the different types of controlinputs or stimuli) and the specific value of each of the variables orparameters (e.g., a specific metric of each input or stimulus) thatsingly or in combination can elicit a desirable and/or substantiallyoptimal response from a biological system, such as a biological sample.In alternative aspects, the biological sample comprises a single cell, acell or tissue culture, or a tissue sample, or an isolated organ,without requiring knowledge of the internal mechanisms of the biologicalsystem.

The systems and methods of the invention can obviate or reduce the needto run a large, e.g., an impracticably large, number of tests.Embodiments can comprise: (i) a device for sustaining cells, cell ortissue cultures, or tissue samples, or organs; (ii) one or moreactuators for stimulating a biological sample, e.g., via chemical,electromagnetic, thermal, mechanical, optical, and/or otherenvironmental stimulation, according to a selected criterion; (iii)) oneor more sensors for measuring the corresponding responses of the samplesresulting from the stimulation; and (iv) a controller executing anoptimization algorithm to modify the actuator's control in puts forsubsequent stimulation responsive to the sensors' outputs responsive tobiological sample responses. Embodiments can also include appropriatesoftware and hardware components to provide user-interface and dataanalysis capabilities to enable a user to monitor and control theautomatic experimentation process.

FIG. 1 illustrates a high level functional block diagram of anembodiment of the invention. In this embodiment, the exemplary devicefor sustaining cells, cell cultures, and tissue samples is amicrofluidic cell culture device (a type of bioreactor) 11. Thisexemplary device can sustain the cells and tissue samples for extendedperiods of time. The length of time required depends on the biologicalprocess and transduction network under investigation, which can be up tomonths or even longer. The exemplary device can provide the basicelements for sustaining biological samples, such as growth factor,cytokine(s), extra-cellular matrix, temperature, pH, gases, and ionsthat are required for normal cell survival and processes. Someembodiments require ongoing monitoring and stimulation of biologicalsamples under test. Embodiments of the sample-sustaining devices canallow integration of corresponding sensors and actuators.

Referring again to FIG. 1, block 12 represents stimuli actuation tostimulate a biological sample within the exemplary microfluidic cellculture (bioreactor) 11 under control of controller 14. Some stimuliactuators may be integrated with microfluidic cell culture 11: forexample, electrodes, temperature actuators, and mechanical actuators(or, in alternative aspects, stimuli actuators may be integrated in anyform of bioreactor). Other stimuli actuator can be separate frommicrofluidic cell culture 11: for example various media and reagentpumps or valves under control of controller 14. Block 13 representssensing responses of the biological sample. In this exemplary embodimentor in any other aspect of the invention, response sensing can include,without limitation: sensing chemicals in cell culture media, sensingintracellular chemicals; sensing electrical fields and membranepotentials, and imaging of the biological sample using bright field,phase contrast, fluorescence, and/or atomic force microscopy. Block 14represents the controlling function, which in some embodiments can beimplemented using a personal computer executing software.

In this exemplary embodiment or in any other aspect of the invention, adevice (e.g., a microfluidic cell culture device) can be fabricated, forexample, by micromolding. In one aspect, the molding ispolydimethylsiloxane (PDMS) (Sylgard 184) on a photoresist master moldto form fluid channels and/or other structures therein. Master molds formicromolding can be fabricated by photolithography of positivephotoresist SJ R 5740 (MicroChem, 4100 I). Three layers of photoresistcan be spun on a glass substrate to achieve a final thickness of 60 μm.After curing, a PDMS replica can be carefully peeled off a master mold.The channels and/or other structures can then be sealed with coverglasses of about 0.17 mm thick. A PDMS replica and a glass piece caneach be oxidized for about 1 min in a plasma cleaner (for example,Harrick, PDC-001) prior to being immediately brought into contact withone another to achieve sealing of the channels in the PDMS replica tothe glass piece. In this exemplary embodiment or in any other aspect ofthe invention, a device, e.g., a microfluidic channel, can be fabricatedwith glass, silicon, or polymeric materials using methods like dryetching, wet etching, laser processes, rapid prototyping, andtraditional machining, using methods that are well known to one ofordinary skill in the art. In one aspect of a system of the inventioncomprising a microfluidic device, least one fluidic chamber or channelis required. Multiple chamber or channel arrays can also be provided formultiple simultaneous experiments.

FIG. 2 illustrates a top view of a microfluidic cell culture device 11according to one embodiment of the invention. The cell culture ismaintained in channel portion 24 for purposes of some examinations andsensings. Meandering channel section 23 is provided to mix inputs 21 and22. Input 22, for example, can be used to introduce growth medium,without any experimental factors, and input 21, for example, can be usedto introduce growth medium with a known concentration of an experimentalfactor. The flow rates of media into inputs 21 and 22, as well as thedesign of meandering channel section 23 can be set to provide desiredtemporal or spatial gradients to channel portion 24.

To sustain the cells and tissue samples for periods of time, abioreactor, e.g., the exemplary microfluidic cell culture (bio reactor)device, can be loaded inside a closed chamber (e.g., Instec Inc,HCS60-STC20A) with temperature control, and adjusted to about 37° C. orsome other selected temperature for the duration of the experiment(unless, of course temperature is an experimental variable, in whichcase the temperature could be varied in a controlled fashion over thecourse of the experiment). In one embodiment, the chamber can be filledwith 5% of CO₂ mixed with air for maintaining the pH value of themedium. The diffusivities for O₂ and CO₂ in PDMS are about 4.1×10·⁵ and2.6×10⁻⁵ cm²/sec respectively, so the ambient atmosphere can affect thesolution within the microfluidic bioreactor.

The embodiment illustrated in FIG. 3 shows microfluidic bioreactor 11mounted within cavity 31 of closed chamber with temperature control 34.The temperature can be controlled by controller 42 responsive totemperature sensor 41 that is operable to measure a temperature ofmicrofluidic bioreactor 11. A pressure sensor (not shown) can also bepositioned to monitor pressure within the microfluidic bioreactor. Gassupply 33 supplies gas to cavity 31 of closed chamber with temperaturecontrol 34 via conduit 43. Mixing manifold 38 combines one or moreincoming media solutions 40 a-40 d (for an example of four) via valvesor metering pumps 39 a-39 d (again for an example of four) in a measuredway.

In alternative aspects, the systems of the invention can comprise use ofvarious media solutions and devices for controlling input and output ofsolutions; for example, different incoming media solutions can containdifferent chemicals and concentrations for either experimental ormanufacturing purposes. The valves or metering pumps 39 a-39 b can beunder the control of controller 42, which can thereby adjust thecomposition of the media being supplied to bioreactor 11. Althoughmixing manifold 38 has been pictured as separate from microfluidicbioreactor 11 in the embodiment as illustrated, mixing manifold 38 canbe integrated with microfluidic bioreactor 11, for example as discussedabove in relation to FIG. 2. Such embodiments can provide an advantagein that the mixed media can be substantially pre-equalized intemperature with the temperature of the bioreactor. In the embodiment ofFIG. 3, the media effluent from bioreactor 11 can pass through sensorblock 35, that can comprise sensors for metabolites, pH, or otherchemical compounds for sensing purposes, that report back to controller42. For the embodiment of FIG. 3, the media effluent is discarded afterpassing through sensor block 35. In other embodiments, the mediaeffluent can be recirculated through the bioreactor. Although mediaeffluent sensor block 35 has been illustrated as separate frommicrofluidic bioreactor 11, at least some of the functions of mediaeffluent sensor block 35 can be implemented within microfluidicbioreactor 11, for example using electrochemical or spectrophotometricsensors. In the embodiment of FIG. 3, a camera 37 is optionally includedand operably connected with controller 42. Camera 37 can be positionedto provide images of a biological sample within bioreactor 11 through awindow in closed chamber with temperature control 34.

In some embodiments, a bioreactor (e.g., the exemplary microfluidicbioreactor) can be mounted on a fluorescence microscope, such as NikonTE200 or TE2000, for real-time monitoring such as illustrated in FIG. 4.In this embodiment, bioreactor 11 is positioned above window 47 intemperature control stage 34 of sealed chamber 45. Atmosphere withinsealed chamber 45 can be provided by gas source 33 via conduit 43. Forsimplicity of illustration, media inflow 55 a and effluent 55 b areshown as being recirculated through media reservoir 40. Drawing elements48 through 53 illustrate a confocal fluorescent microscope of the typethat is well known to one of ordinary skill in the art. Light source 53shines through excitation filter 51 to dichoric beamsplitter 49, thatdirects a portion of the light through object lens assembly 48, thatprojects it on a biological sample in the bioreactor. Fluorescentemissions from the sample return through objective Jens assembly 48,through dichroic mirror 49, and through emission filter 52 to imagecapture device 54. Fluorescence images can be captured, for example,with a 1024×1024 pixel, 16-bit cooled CCD camera (Photometric CH350L).Window and light source 46 schematically indicate a provision forincluding bright field and phase contrast microscope optics inadditional embodiments of the invention.

Referring again to FIG. 3, in some embodiments, mini peristaltic pumps(Instech Inc, P625-1 0638) can be used for metering media inflows. Apressure transducer (Honeywell, ACSX05DN for example), and a temperatureprobe can be operatively coupled to the bioreactor and monitored by acontroller (for example a personal computer with Labview software fromNational Instruments). Fluidic connection(s) of a bioreactor used topractice the systems or methods of the invention can comprise providingfor fresh medium, recirculation of medium, removal—processing—andreplacement of medium (e.g., to harvest a product, such as a recombinantprotein, or a small molecule), removal of old medium or detoxifyingmedium, biochemical stimulation (e.g., with drugs, cytokines, hormones,etc.), or mechanical (shear stress, pressure etc.) control.

Embodiments can be operated within a vertical clean bench such as shownin FIG. 5 to minimize contamination from the external environment. Inthe embodiment of FIG. 5 (shown in side cross-section), the requisiteapparatus can sit on platform 61 within cavity 68 that is partiallyformed by platform 61, back wall 62, partial top wall 65, HEPA filter66, partial front wall 63, and first and second side walls that are notshown. A blower 67 can be operably connected above HEPA filter 66 todirect a filtered flow of air downward toward platform 61 and outwardthrough front aperture 64 in a substantially laminar flow. Becausepositive pressure is maintained within cavity 68, contamination byunfiltered outside air can be minimized even though front aperture 64remains open for access. Such a system can allow long term cultivationof cells and tissue samples with low contamination by outsideparticulate matter and organisms.

Different embodiments can be used for sustaining and growing biologicalsamples and providing stimuli. For example, in a perfusion ormicrofluidic cell culture embodiment, as described above, fluids can becontinuously flowed through the bioreactor to recirculate medium,provide fresh medium, and/or dispense chemical reagents responsive tothe controller. Alternatively, biological samples can be stimulated andmonitored at discrete time points, responsive to the controller. In suchembodiments, biological samples can be replaced in between iterations ofthe processes of the invention, and the medium can be maintained in astatic condition in the between experiments. In such embodiments, otherbioreactor devices, such as tissue culture dishes, Petri dishes, cultureflasks, and multi well plates, can also be used with the invention; inone aspect, proper conditions, such as gas concentrations, pH values,and temperatures, are provided and/or maintained. The cell cultureconditions can be provided and/or maintained by externally placeddevices, such as with an incubator and/or other type of perfusionchamber. The conditions can also be generated and/or maintainedinternally with integrated devices within the bioreactor. Cells andtissue samples can be attached on a surface of the bioreactor.Alternatively, suspension cells can also be circulating in thebioreactor. A peristaltic pump, a syringe pump, or a pressure source canbe used for driving the fluid motion, as are well known to one ofordinary skill in the art. Alternatively, integrated micro pumps such aschemical, mechanical, or electrokinetic pumps such as widely reported inthe open literature covering nanotechnology developments can be used forgenerating fluid motion.

Perfusion manufacturing or experiment embodiments allow for the dynamicapplication of stimuli, such as drugs, inside a device, e.g., abioreactor, such as a microfluidic bioreactor. However, such fluid flowcan create shear stress on channel sidewalls of the device (e.g., amicrofluidic bioreactor). Such shear stress can increase at therelatively small channel dimensions encountered in some microfluidicbioreactors. Cells in such microfluidic bioreactors can experience shearstress. This can be taken into consideration when designingmanufacturing processes or experiments according to various embodimentsof the invention. Various types of cells have been characterized forresponse to such shear stress. The cellular mechanisms for sensing theshear stress are generally unknown, but in designing systems of theinvention it can be taken into consideration that most shear stressesthat can induce biological response changes arc of the same order ofmagnitude, about one to ten dynes/cm². The minimum shear stress forinducing a change in a cell can be cell-type specific. Shear stressesthat arc one to two orders of magnitude smaller than shear stressesknown to elicit physiological response changes can be used as controlsfor different in vitro experiments or manufacturing processes or otherexperiments (e.g., cell-based, ex vivo) according to various embodimentsof the invention.

Various embodiments of the invention provide different types ofactuators to stimulate biological samples under test. Variousembodiments of the invention provide stimulation in space and time.Various embodiments of the invention take into consideration fundamentalcell processes such as cell motility, proliferation, and development,which are regulated by spatial and/or temporal stimulations, eitherbiochemical or physical. For example, the upstroke rate of a pulsatileflow has been reported to affect the gene expression and remodeling ofartery endothelial cells. In general, failure to regulate growth, tocontrol differentiation, or to establish the proper morphologicalconnections can cause ma n y pathological conditions. However,traditional cell cultivation techniques have only limited capabilitiesto investigate such factors.

The ability to produce and apply these diverse stimulation landscapeswith long-term monitoring using systems and methods of the invention isadvantageous to cell study. Embodiments of the present invention candetermine the proper control input parameters to induce desiredbiological responses, emulating natural signals, and/or therapeuticregimens that can regulate biological processes. Therefore embodimentsof the invention provide actuators for generating such stimulationsand/or environment signals. In some embodiments, actuators can generatestimulation with spatial and temporal resolution relevant to abiological process of interest, and can be automatically modified by thecontroller executing an optimization or search algorithm. For example,using microfluidic techniques as discussed above, transient stimulationcan be generated with different cytokines. Concentration or duration ofcytokine stimulation can be adjusted. Implementation of a microfluidicchannel for cellular study can dynamically alter the cell culturemedium, and temporal control of the chemical stimulants can be achieved.

In various aspects of the invention, bioreactors, e.g., microfluidicbioreactor embodiments of the invention, chemical/environmental stimulithat are measured, controlled—manipulated and/or induced include,without limitation, drugs, cytokines, soluble factors, dissolved gases,extra-cellular matrices, cell-cell interactions, neurotransmitters, andvarious ions—in steady state, or in temporal and/or spatial gradients.In some embodiments, a spatial chemical concentration gradient can beachieved by merging of two fluid streams, e.g., in a microchannel(s) ofa device of the invention, so that molecular diffusion can occur betweenthem. In some embodiments, a zigzag channel can extend the fluid pathand hence the diffusion time for developing an approximately linearconcentration gradient. A spatial chemical gradient of such embodimentscan be characterized by merging streams of a dye, such as fluorescein,and another fluid, such as DI water. The concentration gradient of dyeintensity can be experimentally determined by measuring the intensityprofile downstream in the channel A concentration gradient can also byestimated by finite element simulation considering theconvective/diffusive transport of the molecules.

In addition to various chemical stimuli, some embodiments of theinvention can provide physical stimuli such as mechanical forces, nano-or micro-structure environments, physical confinement, light intensitiesand wavelengths, temperatures, magnetic fields, and electric fields tostudy responses of a biological sample. For instance, a shear stressgradient can be generated at an interface of two microchannelcross-sectional areas and/or geometries. In a first embodiment, suddenenlargement of a straight channel produces a shear stress gradientaround the corner of the junction, whereas distal regions of theenlarged section experience lower shear stresses which may be optimalfor the culture of particular cells that can be immobilized in such adistal region. A different velocity landscape can be produced in asecond embodiment having a converging channel. With a constantvolumetric flow rate, the narrowing of the channel increases fluidvelocity in the direction of flow which effectively decreases the shearstress field.

When the ranges of stimuli or environmental parameters that a biologicalsample can tolerate, such the concentration of a generated recombinantpolypeptide, such as an antibody or a cytokine, or small molecule, orthe magnitude of a mechanical force, or the concentration of cells in aculture system, are not known in advance, excessive stimulation orexposure, such as overdose or toxicity, may induce harmful effects tothe samples. Moreover, the history of stimulation can also playimportant role in subsequent responses of biological samples. In suchcases, various embodiments of the invention can use different biologicalsamples of the same type, such as different batches of cells from acommon culture, at different iterations of a study or a manufacturingprocess.

Some embodiments incorporate perfusion systems (e.g., microfluidicperfusion systems) in a bioreactor to practice the invention, e.g., forperforming dynamic stimulation and real-time monitoring of biologicalsamples to optimize or manipulate a biological sample, e.g., productionof a biological product. Sensors can measure not only the magnitude ofresponses but also the dynamic nature of the responses. In someembodiments, such sensors can have high resolutions; for example, downto the single cell or sub-cellular level. Such capability providesinformation in sub-cellular processes, intercellular variations,population distributions, and ensemble averages. In some embodiments,the bioreactor for sustaining and stimulating the biological samples isintegrated with sensors to allow the measurement cell u la r behaviorssuch as cell cycle, morphology, size change, proliferation rate,apoptosis rate, fusion rate, intracellular process. Such embodiments canbe very useful for the study of cell dynamics, or manufacturingprocesses where cell dynamics is important for productivity,particularly where real-time monitoring is required. The dynamics oftranscription factor activity, gene expression, cell proliferation rate,and apoptosis rate in biological samples also can be measured and/ormanipulated when practicing the systems and methods of the invention.For example, in microfluidic bioreactors of the invention, manufacturingprocesses or experiments can be performed to investigate or manipulatethe dynamics of transcription factor activity, gene expression, cellproliferation rate, and/or apoptosis rate in biological samples.

In alternative aspects of the invention, cell induction procedures areperformed through non-invasive monitoring techniques, such as the use ofreporter genes. For instance, the transcription activity of a cell canbe monitored using green fluorescence protein (GPF) reporter. In variousembodiments, sensors can measure the biological responses and the statesof the system continuously, or at discrete time points.

In various embodiments, sensors used to practice the invention caninclude the following types, without limitation; fluorescent (e.g.,ratiometric, intensity, fluorescent decay rate, photobleaching rate,photobleaching after fluorescence recovery); optical (e.g., opticaldensity, colorimetric), electrical (e.g., impedance, current etc.);pressure; temperature, and chemical or biochemical. In variousembodiments, sensors used to practice the invention can measure cellresponse to any internal or external stimuli by, e.g., regulating geneexpression associated with various cellular processes such asproliferation, differentiation and signaling. The systems and methods ofthe invention can incorporate biotechnologies for real-time screening ofgene expression in living cells with any tools, such as a reporter gene(GFP). The systems and methods of the invention can incorporatemicro/nano fabrication technology to optimize microenvironments formanufacturing processes, or for experiments, e.g., for single cellstudies.

For example, in one aspect, the gene expression dynamics oftranscription factor NF-κB and the house keeping gene beta-actin in HEK293T cells can be screened at the translational and transcriptionallevel, respectively. To monitor NF-κB dynamics, the destabilized greenfluorescence protein (d2EGFP, BD Biosciences, Rockville, Md.) can beutilized as a reporter gene.

The systems and methods of the invention can also incorporate use ofoligonucleotide probes, e.g., molecular beacons, which areoligonucleotide probes that have stem-and-loop structures and willundergo a spontaneous fluorogenic conformational change uponhybridization to a complementary nucleic acid target. For example, anoligonucleotide probes, e.g., a molecular beacon, can be used to monitormessage RNA dynamics, e.g., the beta-actin mRNA dynamics measured inthis example. Such a molecular beacon can be extremely specific andsensitive. A molecular beacon specific to any mRNA, e.g., the beta-actinmRNA, can be designed with the help of tools available in the publicdomain such as Basic Local Alignment Search Tool (BLAST) by NationalCenter for Biotechnology Information (NCBI), mfold and RNAstructureprograms. The characterization of the designed molecular beacon can beconducted with the ICYCLER™ (iCycler iQ™) real-time PCR detectionsystem.

In some embodiments, the sensor measurements are used that arenon-invasive to the biological samples and the biological systemrepresented by a biological sample returned to its original state aftereach stimulation iteration. In various embodiments, sensor measurementsare used that are invasive procedures, such as, for example withoutexclusion: cell lysing; transfection; permeabilization of cellmembranes; intra-cytoplasmic injections; illumination withelectromagnetic waves; application of radioactive elements; applicationof fluorescent labeling; fixation; and flow cytometry. These invasiveprocedures can occur in between discrete measurements, or duringcontinuous measurement and/or real time monitoring procedures. Suchinvasive procedures may induce unwanted effects on the system and shouldbe eliminated or minimized. In some aspects, as appropriate, a samplesubjected to such invasive procedures should not be reused during asubsequent test iteration. In some embodiments multiple samples of thesame type can be started in parallel testing. At each test iteration,one of the samples is invasively measured and then discarded, with theiterative testing being continued with the other samples. In this way,measurements can then be done on samples that have not been invasivelytreated at each iteration, but otherwise share a common history.

Because the responses of a biological sample under stimulation can occurafter a time delay, the measurements in the systems and methods of theinvention can incorporate time delay(s). The systems and methods of theinvention can take into consideration the fact that time delay(s) fromstimulation to response for any particular biological sample can be anintrinsic property of that sample, result from the type or magnitude ofstimulus, and/or depend on the history of the biological sample. In thecontrol and optimization systems of the invention, the delay in timeresponse can be considered as latency of a control problem. Whilecharacterization of a biological system's response time itself can beuseful information for inducing a desired response, such information canbe difficult to determine. In such a scenario, the response time itselfcan be considered as system parameter in the optimization processes andsystems of the invention.

Systems and methods of the invention can use optimization or a smartsearch algorithm. These algorithms can be used to process informationfrom biological sample sensor(s) to adjust stimulators. The algorithmscan adjust information for combinations of stimuli to manipulate aresponse from a biological sample, or, to determine, measure or producea more optimal response from a biological sample; this processing can bein a closed-loop fashion. Systems and methods of the invention can useinformation technologies, such as smart search algorithms, to organizeand manipulate data resulting from measurements of responses in complexbiological networks, or generating stimuli to complex biologicalnetworks (which include most bioreactor systems). Systems and methods ofthe invention can use focus on identifying individual components inbiochemical and molecular biological systems and their interactionsthrough signal transduction pathways. Systems and methods of theinvention can use knowledge about complex biological systems to designand apply optimization strategies, e.g., from the field of engineering.Systems and methods of the invention can consider combinations ofstimuli and their interactions in a desired biological system response.In this aspect, the technology is fundamentally different from thetraditional approaches of conducting biological research in which eachthe effect of stimulus tends to be studied independently.

Embodiments of the present invention take a systematic approach to theregulation of complex biological systems, based on responses of abiological sample to different combinations of stimuli. In some aspect,rather than conducting numerous experiments to sample amultidimensional, multivariable stimulus space, and then processingresponse data to seek combinations of stimuli resulting in more desiredresponses, the systems and methods of the invention process responsedata to direct the combinations of stimuli used for subsequentmanufacturing processing steps (e.g., iterative steps), or experiments,to generate subsequent data or manufacturing protocols. This can reducethe n umber of developmental manufacturing processing steps, orexperiments, required for optimization by avoiding conductingexperiments in unpromising directions. In some embodiments, a stochasticsearch algorithm, iteratively determines the inputs (stimuli) forachieving a desired response of the system (biological sample). Suchembodiments go beyond using feedback and control for maintainingsustaining conditions for a biological sample, as is known in the art,by using feedback and control to control the selection of stimuli forexperiments.

Any known stochastic search algorithm can be used in practicing thesystems or methods of the invention; and examples of well-knownstochastic search algorithms include, without exclusion: simulatedannealing; stochastic local search; stochastic hill-climbing;Metropolis-Hastings sampler; greedy randomized adaptive search;evolutionary algorithm; genetic algorithm; taboo search; and/or GurGame, or variations or combinations thereof.

As discussed below, we have demonstrated Gur Game as an effectivestochastic search algorithm in practicing the methods of the invention;see Examples below, for a study of cytokine combination optimization.Much like a genetic algorithm, the Gur Game aims for self-organizationand self-optimization of the system. The essence of the Gur Game,including the forms used in practicing the invention, is a biased randomwalk toward the global optimum.

The essential concept in the Gur Game, including the forms used inpracticing the invention, is the global figure of merit, called thereward function, which measures the performance of the system as awhole. To implement the algorithm, each input (e.g., a cytokine) isrepresented by an automaton with a pre-defined set of discrete states.In general, there is no specific limitation on the choice of the statesin the automaton and different in puts can have different number ofstates and state values. During the study, each automaton move from onestate to another based on being rewarded for their behavior. This“reward” probabilistically drives the system to the desired outputstates. More specifically, the output value (reward value) is comparedto a random number. If the reward value is larger than the randomnumber, the automaton is rewarded. Otherwise, the automaton ispenalized. In other word, the reward values determine the chances thatan automaton is “rewarded” at different iterations. This process isrepeated for each actuator during each iteration. Eventually a globaloptimal state is achieved.

Modifications or variations of a Gur Game, multiple output, multiplereward values, and multiple set of measurements can be used to practicethe invention. In one aspect, the predefined set of discrete statevalues can be changed dynamically according to the current or recentinputs and outputs relationship. In one aspect, the time period of eachiteration can be changed dynamically. As noted above, other algorithmsused to practice the invention include smart non-linear optimizationsearch algorithms such as simulated annealing, stochastic local search,stochastic hill-climbing, metropolis-Hastings sampler, and variationsthereof.

An advantage of stochastic search algorithms are that only minimal priorknowledge of the system to be optimized is required and the controllercan calculate subsequent iteration in puts rapidly. The exemplarysimulated annealing, stochastic local search, stochastic hill-climbing,Metropolis-Hastings sampler, greedy randomized adaptive search,evolutionary algorithm, genetic algorithm, taboo search, Gur Gamealgorithms are all exemplary sampling stochastic search algorithms thatcan be used to practice this invention, and all are well known in theart. Stochastic search algorithms have been shown to be effective in avariety of applications in science and engineering as reported in theopen literature. In one aspect, a stochastic search algorithm caninclude at least one system performance goal, such as a reward function,which an algorithm attempts to optimize (e.g., minimize, maximize or toreach a constant value). This defines a goal for overall systemperformance. Other aspects use algorithms for driving a system towardsbetter output, such as, for example, local gradient, probability of anevent, or “evaporation” of certain parameters. A stochastic searchalgorithm used to practice this invention can also include randomness indirecting search. Such randomness can enable the algorithm escape from alocal optimum when there could be a better local or global optimum.Avoiding entrapment in local optima can be especially important insearching complex biological systems that tend to be noisy, stochastic,active, and/or dynamical. Such randomness in a search algorithm canprovide a generalized way to handle such unknown and unpredictableissues. Embodiments of the invention comprising stochastic searchalgorithms can be especially useful for studying living biologicalsystems, unlike other biochemical systems where randomness is not asimportant.

Various embodiments can employ various methods to implement this randomdecision making. The randomness itself can be within a certain range,adjusting according to a pre-defined way, or adjusting according thedynamic response of the system's parameters.

Other control schemes can be incorporated into other embodiments forstudying biological samples. Such control schemes can be classified bythe level of prior knowledge required in order to implement thealgorithm.

In one model-based control used to practice this invention, a physicalsystem (a plant, in control terminology) is used, as described by amathematical model. Then a controller, e.g., LQR (linear quadraticregulator) or PID (proportional-integral-derivate), can be designedaccording to the output requirements. In one aspect, among all theavailable control schemes, a model based controller can provide the bestperformance assuming the system is described reasonably accurate by themodel and all essential components are considered. However, it can bedifficult to regulate unknown signal transduction networks by usingmodel-based controller without an adequate model.

Another type of controller used to practice this invention involvesidentification of the model and the model parameters. For example,neural network (NN) and design of experiment (DOE) type controllersbelong to this category. In general, the more unknown parameters, themore data is required by the controller. In one aspect, as in a typicalbiological network, the system of the invention uses a large n umber ofdynamic parameters. The fitting parameters are mainly determined byminimizing the deviations between measurements and model predictions. Ifthe basic structure of network is known or large amount of data isavailable, the identification approach can be extremely useful. Forexample, in one aspect, techniques such as neural network are used topredict and control highly nonlinear systems. However, theidentification methods could be misleading for certain networks andover-fitting can occur when high quality experimental data is notavailable.

Another category of controller used to practice this invention is rulebased techniques, such as local search and stochastic search algorithms,such as described in more detail, below.

In practicing the invention, when using stochastic search algorithms,the system for optimization or manipulation can be a response (or set ofresponses) corresponding to a respective set of stimuli for discreteiterations. In one aspect, the stimulation to response time interval fora system for optimization or manipulation can be small compared to theinterval between iterations, this pairing of data can uniquelycharacterize a system at each iteration. On the other hand, if the timeinterval is Jong compared to an iteration, the system's memory of priorstimuli can affect its response to present stimuli. In such casesdynamical or “analog” implementations can be used; i.e., the systemitself determines the proper input waveform/duration and outputmeasurement time. The resulting dimensionality of search space can,however, significantly increase in such cases.

The terms optimum, optimized, and optimization as used herein can referto an improved, or more desirable biosystem response, even though theresponse may actually not be at an absolute optimum.

Computer Systems and Computer Program Products

The invention provides articles (e.g., computer program products)comprising a machine-readable medium including machine-executableinstructions, computer systems and computer implemented methods topractice the methods of the invention. Accordingly, the inventionprovides computers, computer systems, computer readable mediums,computer programs products and the like having recorded or storedthereon machine-executable instructions to practice the methods of theinvention. As used herein, the words “recorded” and “stored” refer to aprocess for storing information on a computer medium. A skilled artisancan readily adopt any known methods for recording information on acomputer to practice the methods of the invention. The methods of theinvention can be practiced using any program language orcomputer/processor and in conjunction with any known software ormethodology. Some embodiments can use analog, or hybrid analog/digitalcircuitry for feedback and control, such as for example, neuralnetworks, in which the control algorithm could be “hard wired.” The termcomputer product as used herein can refer to any of such aboveembodiments.

Another aspect of the invention is a computer readable medium havingrecorded thereon machine-executable instructions to practice the methodsof the invention. Computer readable media include magnetically readablemedia, optically readable media, electronically readable media andmagnetic/optical media. For example, the computer readable media may bea hard disk, a floppy disk, a magnetic tape, CD-ROM, Digital VersatileDisk (DVD), Random Access Memory (RAM), or Read Only Memory (ROM) aswell as other types of other media known to those skilled in the art.

The computer/processor used to practice the methods of the invention canbe a conventional general-purpose digital computer, e.g., a personal“workstation” computer, including conventional elements such asmicroprocessor and data transfer bus. The computer/processor can furtherinclude any form of memory elements, such as dynamic random accessmemory, flash memory or the like, or mass storage such as magnetic discoptional storage. For example, a conventional personal computer such asthose based on an Intel microprocessor and running a Windows operatingsystem can be used. Any hardware or software configuration can be usedto practice the methods of the invention. For example, computers basedon other well-known microprocessors and running operating systemsoftware such as UNIX, Linux, MacOS and others are contemplated. As usedherein, the terms “computer,” “computer program” and “processor” areused in their broadest general contexts and incorporate all suchdevices.

The invention will be further described with reference to the followingexamples; however, it is to be understood that the invention is notlimited to such examples.

EXAMPLES

The following examples are provided to illustrate, but not limit,embodiments of the invention. In the first example, nearly optimalcombinations of cytokines are determined for regulating transcriptionactivity. This example represents a use of an embodiment of the presentinvention for additional applications of the invention, e.g., vaccineproduction optimization, drug screening, determining therapeuticregimens for the treatment of diseases, toxicity studies, and the like.In the second example, another embodiment of the invention is used todirect activation and differentiation of neural stem cells for tissueengineering applications. In the third example, a method formanipulating and discovering artificial gene and metabolic networks isdescribed. The third example illustrates the use of an embodiment thepresent invention to mimic and study biological processes.

Example 1 Optimizing a Combination of Cytokines to RegulateTranscription Factor NF-κB.

As a first example, we applied the Gur Game algorithm (one of theexemplary optimization algorithms used to practice the invention) forregulating the activity of a polypeptide, a transcription factor callednuclear factor kappa B (NF-κB). This example demonstrates that thesystems and methods of the invention can effectively measure andmanipulate environmental parameters in a bioreactor, and manipulate andoptimize a biological system (in this example, regulating the expressionand/or activity of a polypeptide).

NF-κB regulates expression of numerous genes that mediate survival,apoptosis, proliferation, inflammatory response, and oncogenesis, and isreported to be one of the major drug targets for cancer and chronicinflammatory diseases. The NF-κB activity can be controlled by severalsignal transduction pathways and numerous stimuli can trigger theactivities of one or more kinases that activate NF-κB. Determining a setof cytokines and growth factors that exerts desired effects on NF-κB canbe a crucial step for realizing the therapeutic potential of targetingNF-κB. At the same time, the nonlinear interactions among the pathwaysand the large parametric space constituted by the combinatory cytokinesimpose a major challenge.

An advantage of an embodiment using the Gur Game as a search algorithmfor implementation on the controller is that the Gur Game does notrequire an a priori model of the system. Gur Game can be robust and usedto make random changes in the system and the environment. Therefore,exemplary systems and methods of the invention comprising use of GurGame as an optimization algorithm is good choice for controlling complexbiological systems for which adequate modeling of the governing networksis lacking. The Gur Game can be viewed as being based on biased randomwalks of multiple finite state automata toward higher systemperformance, as defined by a reward function. The asymptotic behavior ofthe automata has been modeled using the Markov chain analysis. Thereward function of the system can be multi-modal, nonlinear, and evendiscontinuous.

To implement the Gur Game algorithm, each cytokine was represented by anautomaton with 10 discrete states (0, 0.25, 0.5, 1, 2.5, 5, 10, 25, 50,and 100 ng/ml) as shown in FIG. 6. At each iteration, a combination ofcytokines at their respective states was applied to stimulate the cellsfor one hour. The transient response of GFP intensity was observed toapproach a maximum at about seven hours after stimulation, coincidingwith reports in the open literature. The choice of cytokines orcombination of cytokines did not show an observable effect on thetransient response of the fluorescence intensity. The GFP expressionkinetics is thought to be controlled by protein folding and degradationrates, while the amount of transcription (GFP intensity) is thought toprovide an indication of the NF-κB activity. The peak intensities werenormalized as the reward function for Gur Game. The reward function mapsthe system state from 0 to 1, with high GFP intensity corresponds tovalues nearer 1. For each automaton, a random n umber from 0 to 1 ischosen and compared with the reward value. The next states of theautomata were determined according to a prescribed manner (see FIG. 1).The process was repeated for each cytokine during each iteration.

Referring again to FIG. 6, each cytokine as represented by an automaton.The numbers represent the concentration of the cytokines, in ng/ml.During each iteration, a random number was generated for each automaton.The numbers were compared to the reward value. If the reward value waslarger than the random n umber, the automaton was rewarded (win).Otherwise, the automaton was penalized (lose). The automaton thendecided the state in the next iteration according to the automatondesign. Generally, the state moves toward center if penalized, and awaythe center if rewarded.

Microfluidic systems have been proven to be effective for manipulatingdifferent biological objects, such as cells and molecules. In thisexample, the optimization experiments were conducted inside microfluidicchannels to facilitate real-time monitoring and dynamic stimulation ofthe cells. At each iteration, the cells were transiently stimulated witha combination of agonists for one hour as shown in FIG. 7A. (In FIG. 7a, the concentrations of individual cytokines are indicated as follows:TNFα concentrations are plotted as solid squares; TNF concentrations areplotted as solid circles; IL-1α concentrations are plotted as solidtriangles; IL-1β concentrations arc plotted as inverted triangles; EFGconcentrations are plotted as open squares; and BAFF concentrations areplotted as open circles.) The initial concentrations of the cytokineswere 2.5 ng/ml. In order to monitor the activity of the NF-κB, a plasmidwith a green fluorescent protein (GFP) reporter gene was placed in the293T cell. The GFP expression is controlled by a promoter whichconsisted of a kappa enhancer element (KB4). Similar configurations havebeen applied in other studies where the GFP fluorescence intensity wasable to reflect the cellular NF-κB activity. In addition, computationalanalysis was performed to correlate the relationship between theexpression level of GPF and the activity of NF-κB. Numerical simulationindicated the best duration and time for cytokine stimulation and GFPintensity measurement. The GFP fluorescence intensity of each cell wasrecorded 7 hours after the stimulation as shown in FIG. 7B (datarepresent the average intensity of 40-128 cells).

The Gur Game was used to determine the cytokine concentrations insubsequent iterations to probabilistically drive the system toward ahigh GFP output, i.e., high NF-κB activity. FIG. 6 is a flow chartillustrating an exemplary Gur Game Algorithm, the algorithm used in thisexample. A detailed description of this exemplary algorithm is set forthabove. The peak intensities were normalized as the reward function forthis exemplary Gur Game algorithm. The reward function maps the systemstate from 0 to 1, with high GFP intensity corresponds to valuesnearer 1. For each automaton, a random number from 0 to 1 is chosen andcompared with the reward value.

The next states of the automata were determined according to aprescribed manner, as illustrated in FIG. 7. The process was repeatedfor each cytokine during each iteration. Each cytokine as represented byan automaton. The numbers represent the concentration of the cytokines,in ng/ml. During each iteration, a random number was generated for eachautomaton. The numbers were compared to the reward value. If the rewardvalue was larger than the random number, the automaton was rewarded(win). Otherwise, the automaton was penalized (lose). The automaton thendecided the state in the next iteration according to the automatondesign. Generally, the state moves toward center if penalized, and awaythe center if rewarded.

Referring to FIGS. 8a and 8b , initially, the system rejected severalcytokine combinations with seemingly high outputs. At iteration 17, thesystem determined a potent combination of cytokines for activating theNF-κB signal transduction pathways. Due to the random walk nature of theGur Game, the system did not settle even with the large performancegains at iteration 17. The algorithm continually searched for otherstates with better performance. It is seen that the reward functiondecreased significantly for several iterations during the search.However, the system robustly returned to the similar NF-κB activity atiterations 23 & 28. This most potent cytokine combination, which wasdetermined by the final probability distributions of the cytokineconcentration (see supplementary material), was TNFα=25 ng/ml, TNFβ=50ng/ml, IL-1α=50 ng/ml, IL-1β=25 ng/ml, EGF=2.5 ng/ml, BAFF=2.5 ng/ml.The robustness of the Gur Game algorithm is further illustrated by theobservation that the paths by which the cytokine combinations movedtoward the peak were different. In principle, an even larger parametric(input variable) space, e.g., 7, 8 or more cytokines with 10⁷, 10⁸ ormore combinations, is expected to achieve a similar rapidly convergingrate by using the stochastic searching in a feedback loop. FIG. 8C showsnormalized GFP intensity at different iterations for the cytokinecombination and individual cytokine (TNFα). In FIG. 8C, the dynamicresponses of NF-κB activity for cells treated with the cytokinecombination are plotted as solid circles, the dynamic responses of NF-κBactivity for cells treated with TNFα 50 ng/ml are plotted as asterisks,and the control dynamic responses of NF-κB activities are plotted asopen circles. Data represent the mean±SEM (standard error of the mean,numerically:

${S_{\overset{\_}{x}} = \frac{S}{\sqrt{n}}},$

where S is the standard deviation and n is the number of measurements)from at least 100 cells inside the microfluidic channels.

With the most potent combination of cytokines efficiently determined bythe closed-loop feedback control scheme of the embodiment, theconcentration of a specific cytokine was then varied, while theconcentrations of the others were held constant to understand thesensitivity of the specific cytokine as shown in FIGS. 9A through 9F.For FIGS. 9A through 9F, the data show mean±SEM of at least 300 cells.Experiments were conducted in 96-well plates. The cells were stimulatedwith the appropriate concentration of cytokines for one hour and washedwith fresh media. Fluorescence measurements were carried out 7 hoursafter stimulations.) TNFα was found to be the most sensitive in thecombination in affecting the activity of NF-κB. Elimination of TNFα inthe cytokine combination resulted in a ˜50% decrease in fluorescenceintensity. Total elimination of any one of TNFβ, IL-1α or IL-1β resultedin −30% decrease in fluorescence intensity. The sensitivity curves ofTNFα and β (FIGS. 9A and 9B) exhibited “peaky” patterns while the curvepatterns for IL-1α and β (FIGS. 9C and 9D) were smoother. The effects ofIL-1α and IL-1β were not sensitive to their concentrations in the rangeof 25-50 ng/ml. When combined with the potent cytokine combination, EGFdecreased the NF-κB activity with increasing dose concentration (FIG.9E). It should be noted that EGF alone did not show a strong effect onthe NF-κB activity in 293T cells (data not shown). For the case of BAFF,it had a minimal effect on NF-κB activity with or without the potentcytokine combination (FIG. 9F). It is interesting to note that the GurGame search algorithm suggested lower and lower concentrations of bothEGF and BAFF as the iterations proceeded (FIG. 7A). These data clearlyindicate that the effects of individual cytokines are not additive inthe combinatory tests and the interactions among pathways are nonlinear.

Example 2 Directed Neural Stem Cell Differentiation with Biochemical andPhysical Stimuli by an Intelligent Closed-Loop Control Algorithm

As a second example of the use and effectiveness of an exemplaryembodiment of the invention, the activities and differentiation of stemcells were investigated, specifically the activities and differentiationof neural progenitor cells (NPCs), which are promising areas for tissueengineering applications. This exemplary example demonstrates that thesystems and methods of the invention can be used to determine parametersfor the direction of cell behavior, e.g., stem cell behaviors. Thisexample demonstrate that the compositions and methods of the inventioncan be used for systems manipulation and/or optimization of a biologicalmanufacturing, cell engineering, or experimental systems related to cellbehavior, including cell growth or differentiation, e.g., inbioengineering systems for reconstructing or regrowing tissue or organsystems, e.g., in nervous system, muscle or liver damage repair, or, inbioreactors for the production of proteins, polypeptides or peptides forvaccines; including manipulation and/or optimization of systems fordifferentiating cells to a desired state or having a specific property,e.g., ability to produce or modify a polypeptide, e.g., a therapeuticprotein, polypeptides or peptides for vaccines, and the like.

A promising approach for treatments of nervous system damages iscell-replacement therapy. Several recent studies have been described inthe open literature on transplantation of neural precursor/stem cellsinto the rodent model of spinal cord injury. While cell transplantationtherapy has shown promises as a clinical treatment, the approachprocesses several concerns. For instance, transplantation ofundifferentiated cells could result in low engraftment efficiency andtherefore reduced clinical efficacy. Undifferentiated progenitor cellsmay cause spontaneous differentiation into undesired lineages. Mostimportantly, it processes the risk of teratoma formation, in the case ofembryonic stem cells. Ex vivo differentiation of NPCs provides apromising alternative, which may minimize the previously mentionedconcerns. For differentiation of neural cells prior to transplantation,the commitment could be overridden by environmental cues in the injuredspinal cord. Large scale directed differentiation, separation, andpurification of target cells still presents challenges. It is generallybelieved that more basic and preclinical research must be done beforeattempting human trials using stem cell therapies to repair the damagednervous system. There are many fundamental questions still to beanswered either for developing replacement cells or activating thebody's own stem cells in vivo. An embodiment of the present inventionprovides a unique tool to determine parameters for the direction of stemcell behaviors.

One embodiment of the present invention can be used to systematicallyinvestigate the effects of cytokines, growth factors, extracellularmatrix environment, and electrical activity on growth anddifferentiation. Similar to most biological systems, a stem cell'sactivity and differentiation can depend on a large number of exogenousparameters, in which stimuli, their respective magnitudes, theirrespective temporal and spatial gradients, and their interactions can beimportant to the determination of a differentiated phenotype and thefate of the differentiated phenotype. Embodiments of the presentinvention can mimic in vivo environments and search for optimum (ornearly optimum) stimulation to direct stem cells to desired expressions.With these apparatus and method embodiments, NPCs' physiology that isconcerned with the cytokine stimulation, as well electrical activityassociated with living cells and involved in their functional activitycan be studied in a fast pace. Important mechanisms of NPCs response tocytokines and electrical activity, such as depolarization ion,electrodeformation, and associated signal transduction pathway can bestudied in detail. Migration, proliferation, neurogenesis, andremyelization of NPCs under external electric fields, cytokineexposures, and ECM can be characterized. The information obtained canenable the selection of operating parameters of electrical and otherbiomedical stimulation in clinical therapeutic approaches, such asneural prosthesis, stem cell implantation, and stimulation of endogenousprogenitor cells for the replacement of lost neural functions. ECM isextracellular matrix. ECM is a complex network of polysaccharides andproteins secreted by cells and a structural component of tissues thatalso influences their development and physiology.

In one aspect, cells can be obtained from human fetal tissue between 14and 21 weeks post conception, and then cultured. NPCs can also beisolated and cultured from human brain tissue for the first severalyears of life. The NPCs can be cultured in (DMEM):HAMS F12 at about(50:50), gentamicin at about 30 mg/ml, amphotericin B at about 15 μg/ml,human recombinant basic fibroblast growth factor (FGF-B) and epidermalgrowth factor (EGF) both at about 20 ng/ml, and N2 at about 1:100.

Before each experiment, the cells are incubated in an approximately 5%CO₂ atmosphere incubator at approximately 37° C. for about 24 hours,allowing them to recover from cryopreservation prior to use in theadhesion and proliferation assays. The cells can then be cultured on amicrofluidic bioreactor device, according to an embodiment of theinvention. Actuators, such as micro- and nano-electrodes, extracellularmatrices, growth factors, and differentiation factors, can be integratedinto microfluidic bioreactor. These stimuli can be directly control witha computer serving as a controller. The cell's behavior, includingmigration, proliferation, and lineage specific markers (Tuj 1 forneuron, RIP for oligodendrocyte and GFAP for astrocytes) can bemonitored in real time and continuously and served as biologicalresponses from which reward functions (in Gur Game and other stochasticsearch/nonlinear optimization terminology). The information obtained inthe measurement can then be fed into the Gur Game algorithm or otherstochastic search algorithms to determine next iteration stimuli in thesearch for better combinations of stimuli for more nearly optimalresponses. Synergistic and antagonistic effects of stimuli can also bestudied in this way.

As described above, this embodiment of the invention can determineoptimal (or nearly optimal) operating parameters of electrical and otherbiochemical stimulation to direct the neural progenitor cells todifferentiate into cells with desired phenotypes. This can pave the wayfor novel treatments of degenerative diseases of the nervous system. Oneset of goals is to determine the proper conditions for thedifferentiation of NPCs to specific neural cells, such asoligodendrocytes and motor neurons, for SCI and ALS treatments. Whiledirect differentiation of NPCs is clearly useful, other aspects of theneural activity can also be considered. In many spinal injuries, thespinal cord is not fully damaged and some of the signal-carryingneuronal axons are intact. However, the surviving axons no longer carrymessages because oligodendrocytes are lost. One possibility is to directthe migration or directly implant neural progenitor cell to the injurysite and stimulate their differentiation in situ into appropriatesupporting cells, such as oligodendrocytes. Another possibility is todifferentiate neural progenitor cells in vitro and direct thedifferentiated cells into the injury site. With various embodiments ofthe present invention, many important aspects of the neural processescan be investigated, including for example: (i) the migration of NPCsand glia cells; (ii) the survival and proliferation of NPCs; (iii) thedifferentiation of NPCs to specific cell types of the nervous system;and (iv) remylenation of differentiated oligodendrocytes.

Example 3 Iterative Methods for Discovering Manipulating and OptimizingNatural and Artificial Gene Networks

This example demonstrates that the systems and methods of the inventioncan be applied to design, manipulate, understand and/or optimize (ornearly optimize) natural and artificial gene and metabolic networks,including neural networks and similar biological systems, such asmetabolic, growth, apoptotic or differentiation networked systems. Thisexample also demonstrates that the systems and methods of the inventioncan be applied to mimic in vivo neural, gene and/or metabolic networks.

In nature, biological systems such as gene and metabolic networks havebeen observed to express themselves in various ways, such as circadianclocks and auto-regulated gene expressions, for example. It is of greatinterest to understand, control, and mimic such gene and metabolicnetworks both in living organisms (in vivo) and artificially (in vitro).However, such networks tend to be sensitive to a large number ofintercellular factors (e.g., secreted factors, quorum, etc.),intracellular factors (e.g., genes, promoters, pathway components,etc.), and extra cellular parameters (e.g., carbon sources, pH,temperature, light intensity, and other biochemical reagents etc.).Without a detailed understanding of such stimuli and their interactions,it is difficult to understand, control, and/or mimic the robustness,functionality, and properties of these metabolic networks. Embodimentsof the present invention can obviate the need for a full understandingof metabolic networks, while indicating appropriate environment cues(stimuli) for desired metabolic network responses.

Oscillatory networks in E. coli have been described in the openliterature, however the role and influence of environmental cues(stimuli) remain unclear. An embodiment of the present invention can beused to determine external signals required to activate and/or modulatean oscillatory network (in this or any other system). E. coli can becultured in microfluidic systems fabricated by micromolding ofpolydimethylsiloxane (PDMS) on photoresist master. In an embodiment of amicrofluidic bioreactor, as described above, Poly-L-lysine (Sigma, P8920) can be applied at desired locations in the microfluidic deviceusing a pipette or a sharpened glass capillary to achieve selective celldepositions. Patterns on the order of 10 μm can be printed with thismethod. For instance, patterns of roughly 500 μm (field of view of theoptical system) can be printed in order to trap multiple cells forstatistical analysis. The channel can then sealed by a piece of coverslip. Before sealing, holes can be drilled on the glass forinterconnections. The microfluidic channel can be washed by flowingphosphate buffered saline (PBS) for about 5 min. The solution can thenbe switched to about 10 mg/ml of bovine serum albumin in PBS for about 5min. This process can reduce the non-specific binding of cells the inthe channel The channel can then be filled with a suspension of the E.coli cells and the cells are allowed to adhere to the channel surfacefor about 5 min. Cell adhesion occurs preferentially in regions withpoly-L-lysine modification. The number of cells adhered on the channelsurface can be adjusted by adjusting the cell concentration in thesolution and residency time of the cells in the channel Non adherentcells can then be removed by washing with culture medium.

The microfluidic chip (bioreactor, see FIG. 2) can be loaded onto anepi-fluorescence microscope (for example a Nikon TE 200™) equipped withan ultraviolet light source (e.g., a 100 W mercury lamp). Athermolectric hot and cold stage (e.g., Instec, HCS60 with a platinumRTD temperature control sensor) can be mounted on the microscope. Thiscan be used to maintain the temperature of the microfluidic chip atabout 37° C. with a PID feedback controller (for example, Instec,STC200™) for the gene expression experiments. The two inlets of themicrofluidic chip (21 and 22 of FIG. 2) can be connected to reservoirsof desired fluids. A peristaltic pump (e.g., Instech, P625) can beconnected at the outlet (25 of FIG. 2) for fluid aspiration. Theconfiguration allows for substantially identical flow rates from the twoinlets, thereby promoting the generation of a stable chemical gradientin zig-zag section 23 (FIG. 2) of the microfluidic chip 11. The flowrate can be adjusted from about 1-400 μl/min and can be calibrated witha pressure transducer (e.g., Honeywell, ACSX™ series).

Fluorescence and bright-field images can be imaged by, for example, a 16bit, 1024 by 1024 pixel cooled CCD camera (such as a PhotometricCH350UM) periodically at a predefined period of roughly 1-5 minintervals and recorded on an appropriate medium. Light sources can beshuttered between exposures. Approximately two to three hundreds cellscan be captured in the field of view in one image. This embodiment canbe combined with image analysis software for automated processing.

Spatial chemical gradients can be generated by merging streams of mediaand reagents of various concentrations. The concentration gradients ofchemical can be experimentally determined by fluorescence intensity.Different concentrations of IPTG in M9 medium can be tested. The inducerconcentration distribution in the gene expression experiment can beestimated by numerical simulation (CFDRC) with diffusivity 8.80×10⁻⁶cm²/sec for IPTG at 37° C.

Stimuli, such as inducers, carbon sources, temperature, pH, celldensities; and internal gene circuit parameters, such as promoteractivities, transcription rates, translation rates, maturation rates,and metabolic reaction rates, can be studied and tested with thisembodiment. The biological sample outputs, such as GFP intensity,frequency, number of mitosis cycles, and cell-cell variations, can bemonitored and recorded with a CCD camera in some embodiments. The GurGame algorithm or other stochastic search algorithm can be applied tooptimize (or nearly optimize) stimuli to elicit a desired response fromthe biological sample in various embodiments (for example, maximizingthe frequency of GFP oscillation frequency) Since biochemical reactionscan generally be speeded up by increasing temperature, it is likely thattemperature is an important factor for the maximizing the frequency.Typically, bacteria are cultured at 30° C. to 37° C., which is areasonable range for initial testing. However, increase in temperaturemay alter the dynamics of the reactions and overheat enzymes, therebyreducing their activities. The doubling rate, which affects the proteindynamics in a variety of ways, can also be changed. Other parameters,such as pH, can also affect a metabolite's transportation rates andother reaction kinetics and, therefore, the overall performance(frequency) of the gene network. In general, the critical parameters areunknown and the interactions of these parameters are unclear.

Embodiments of the present invention with a stochastic search algorithmcan provide a systematic method for determining the optimal combinationfor maximizing the frequency and provide important insights forunderstanding the important parameters (stimuli) and their interactions.

It will be appreciated that the above description for clarity hasdescribed exemplary embodiments of the invention with reference todifferent functional units and processors. However, it will be apparentthat any suitable distribution of functionality between differentfunctional units or processors may be used without detracting from theinvention. For example, functionality illustrated to be performed byseparate processors or controllers may be performed by the sameprocessor or controllers. Hence, references to specific functional unitsare only to be seen as references to suitable means for providing thedescribed functionality rather than indicative of a strict logical orphysical structure or organization.

The invention can be implemented in any suitable form includinghardware, software, firmware or any combination of these. The inventionmay optionally be implemented at least partly as computer softwarerunning on one or more data processors and/or digital signal processors.The elements and components of an embodiment of the invention may bephysically, functionally and logically implemented in any suitable way.Indeed the functionality may be implemented in a single u nit, in aplurality of u nits or as part of other functional units. As such, theinvention may be implemented in a single unit or may be physically andfunctionally distributed between different units and processors.

Although the present invention has been described in connection withsome embodiments, it is not intended to be limited to the specific formset forth herein. Rather, the scope of the present invention is limitedonly by the accompanying claims. Additionally, although a feature mayappear to be described in connection with particular embodiments, oneskilled in the art would recognize that various features of thedescribed embodiments may be combined in accordance with the invention.In the claims, the term comprising does not exclude the presence ofother elements or steps.

Furthermore, although individually listed, a plurality of means,elements or method steps may be implemented by e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent claims, these may possibly be advantageously combined, and theinclusion in different claims does not imply that a combination offeatures is not feasible and/or advantageous. Also the inclusion of afeature in one category of claims does not imply a limitation to thiscategory but rather indicates that the feature is equally applicable toother claim categories as appropriate. Furthermore, the order offeatures in the claims does not imply any specific order in which thefeatures must be worked and in particular the order of individual stepsin a method claim does not imply that the steps must be performed inthis order. Rather, the steps may be performed in any suitable order.

The figures provided are merely representational and may not be drawn toscale. Certain proportions thereof may be exaggerated, while others maybe minimized. The figures are intended to illustrate variousimplementations of the invention that can be understood andappropriately carried out by those of ordinary skill in the art.

Therefore, it should be understood that the invention can be practicedwith modification and alteration within the spirit and scope of theappended claims. The description is not intended to be exhaustive or tolimit the invention to the precise form disclosed. It should beunderstood that the invention can be practiced with modification andalteration and that the invention be limited only by the claims and theequivalents thereof.

What is claimed is:
 1. A system, comprising: a processor; and a memorystoring processor-executable instructions, which when executed by theprocessor direct the processor to: provide control data indicatingapplication of a stimulus to a biological system; obtain sensor dataindicating measurements of a response of the biological system to thestimulus; determine fitting parameters of a biological system modelbased on the response of the biological system to the stimulus; andpredict a magnitude of the stimulus that, when applied to the biologicalsystem, will yield an optimized response of the biological system basedon the model.
 2. The system of claim 1, the instructions furthercomprising instructions to direct the processor to: determine, based onthe model, predicted responses of applying varying magnitudes of thestimulus to the biological system; obtain measured responses of thebiological system to the varying magnitudes of the stimulus; anddetermine ones of the fitting parameters by reducing deviations betweenthe predicted responses and the measured responses.
 3. The system ofclaim 1, the instructions further comprising instructions to direct theprocessor to: determine a combination of stimuli to apply to thebiological system based on the model; and direct the combination ofstimuli to be applied to the biological system.
 4. The system of claim3, wherein the combination of stimuli include one or more ofbiochemical, electromagnetic, thermal, mechanical, and opticalstimulation.
 5. The system of claim 3, wherein the combination ofstimuli includes a combination of drugs.
 6. The system of claim 1, theinstructions further comprising instructions to direct the processor to:determine a predicted response time of the biological system to thestimulus, based on the model.